Automated Blood Cell Detection and Counting Based on Improved Object Detection Algorithm

Blood cell detection and enumeration play a crucial role in medical diagnostics. However, traditional methods often face limitations in accurately detecting smaller or overlapping cells, which can result in misclassifications and reduced reliability. To overcome these challenges related to detection failures and classification inaccuracies, this study presents an enhanced YOLO-based algorithm, specifically designed for blood cell detection, referred to as YOLO-BC. This novel approach aims to improve both detection precision and classification accuracy, particularly in complex scenarios where cells are difficult to distinguish due to size variability and overlapping. The Effective Multi-Scale Attention (EMSA) is integrated into the C2f module, enhancing feature maps by applying attention across multiple scales to refine the representation of blood cell features. Omni-Dimensional Dynamic Convolution (ODConv) is employed to replace the standard convolution module, adaptively combining kernels from multiple dimensions to improve feature representation for diverse blood cell types. For the experiments, the BCCD (Blood Cell Count and Detection) dataset is utilized, alongside data augmentation techniques. In terms of experimental evaluation, YOLO-BC outperforms YOLOv8 with a 3.1% improvement in mAP@50, a 3.7% increase in mAP@50:95, and a 2% increase in F1-score, all based on the same dataset and IoU parameters. Notably, small objects such as platelets are also detected with high accuracy. These findings highlight the effectiveness and potential clinical applicability of YOLO-BC for automated blood cell detection.